The preservation and restoration of cultural heritage has acquired increasing attention in recent years since it has priceless value from both historical and touristic points of view. However, this activity requires considerable funds to be carried out, and frequently, such costs cannot rely entirely on public sources. At the same time, crowdfunding platforms are becoming a widely recognized way to collect funds and finance projects. Indeed, in the literature, some attempts have been made to use crowdfunding platforms to support renovation and restoration projects for cultural heritage items. Even if the benefits of their use in general, particularly for cultural heritage, are widely recognized, skepticism remains regarding transparency, reliability, and trustworthiness. In this regard, the emerging blockchain technology could represent an innovative solution for promoting and guaranteeing such properties through the entire crowdfunding process. However, existing solutions based on the direct use of cryptocurrencies for collecting funds have encountered users' fear and reluctance due to their novelty and the absence of clear and complete regulation by governments. For this reason, in this paper, we propose a solution that is not based on using cryptocurrencies but concentrates on the immutability, traceability, and trustworthiness properties that blockchain offers. To do so, an integrated solution is proposed that combines traditional platforms with a set of smart contracts and a Decentralized Application (dApp), allowing the immutable storage of information inside the blockchain and their subsequent validation by the donors.

Understanding tourist behavior patterns is crucial for developing effective recommendation and decision support systems. The behaviors are often captured through the trajectories followed by tourists during their journeys or the sequences of visited Points of Interest (PoIs). Identifying common patterns and tracking their evolution over time can enhance the ability to understand, predict, and influence tourist choices, ultimately supporting goals like promoting specific destinations and fostering sustainable visitation patterns. Clustering algorithms like k-Means are commonly used to extract frequent patterns, requiring a tailored distance metric suited to the task. Since tourist trajectories combine spatial, temporal, and semantic features, defining a distance function that accurately captures these multifaceted aspects is essential. This paper examines various methods for encoding trajectory data and explores their effects on the clustering process. Finally, we compare and validate their suitability by using a real-world dataset of visits performed by tourists in Verona (Italy) from 2014 to 2022.

In the era of massive sharing of information, the term social provenance is used to denote the ownership, source or origin of a piece of information which has been propagated through social media. Tracking the provenance of information is becoming increasingly important as social platforms acquire more relevance as source of news. In this scenario, Twitter is considered one of the most important social networks for information sharing and dissemination which can be accelerated through the use of retweets and quotes. However, the Twitter API does not provide a complete tracking of the retweet chains, since only the connection between a retweet and the original post is stored, while all the intermediate connections are lost. This can limit the ability to track the diffusion of information as well as the estimation of the importance of specific users, who can rapidly become influencers, in the news dissemination. This paper proposes an innovative approach for rebuilding the possible chains of retweets and also providing an estimation of the contributions given by each user in the information spread. For this purpose, we define the concept of Provenance Constraint Network and a modified version of the Path Consistency Algorithm. An application of the proposed technique to a real-world dataset is presented at the end of the paper.

Workflow technology has emerged as one of the leading technologies in modeling, redesigning, and executing business processes in several different application domains. Among them, the representation and management of health and clinical processes have been attracting a growing interest. Such processes are in general related to the way each health organization provides the required healthcare services. Health and clinical processes underlie the specification and application of clinical protocols, clinical guidelines, clinical pathways, and the most common clinical/administrative procedures. Current workflow systems are lacking in effective management of three general key aspects that are common (not only) in the clinical/health context: data dependencies, exception handling, and temporal constraints. For example, a laparoscopic intervention may need the results of the concurrent bioptic analysis to be properly concluded while exceptional recovery activities have to be performed in case of emergency evidence during standard treatment; however, the successful application of a fibrinolytic therapy requires a maximum delay of 30 min after the admission into the emergency department. In this paper, we propose TNest, a new advanced, structured, and highly modular workflow modeling language that allows one to easily express data dependencies and time constraints during process design, in addition to exception handling and compensation activities. As for temporal constraints, we focus here on temporal controllability which is the capability of executing a workflow for all possible durations of all tasks satisfying all temporal constraints. Moreover, we analyze the computational complexity of the temporal controllability problem in TNest, and we propose a general algorithm to check the controllability. All the features of TNest that have been considered to model clinical pathways from classical clinical guidelines, i.e., those features for the management of STEMI patients, published by the American College of Cardiology/American Heart Association, will be used throughout the paper as a motivating scenario.